Python Developer with GenAI

We are seeking a Python Developer with experience in Generative AI to join a team delivering backend services and AI-enabled solutions. The person in this role will design and implement backend applications, integrate REST APIs, and work with large language model (LLM) based systems, including agentic AI and orchestration.

Responsibilities

• Design, develop and maintain backend services and applications using Python.
• Develop and integrate RESTful APIs, with practical experience in FastAPI or equivalent frameworks.
• Architect, implement and integrate solutions based on LLMs and Generative AI, including agentic AI, AI agent orchestration and tool integration.
• Implement prompt and context management strategies and produce structured outputs suitable for downstream processing.
• Evaluate, monitor and iterate on AI solutions to ensure performance, reliability and safety.
• Collaborate with international clients and distributed teams to define requirements, deliver solutions and provide technical guidance.
• Contribute to CI/CD pipelines, automated testing and deployment processes using Azure DevOps or equivalent tools.

Qualifications

• Commercial experience in Python development and backend application design.
• Proven experience creating and integrating REST APIs; familiarity with FastAPI is required.
• Practical experience with LLM / Generative AI solutions, including:
• Agentic AI / AI agents and their orchestration;
• Integration of agents with external tools;
• Prompt and context management;
• Designing for structured outputs;
• Understanding of LLM operation and lifecycle;
• Evaluation and monitoring of AI systems.
• Project experience with at least one major public cloud provider; Azure is preferred, AWS is also acceptable.
• Practical knowledge of CI/CD, automated testing and deployment (e.g., Azure DevOps or equivalent).
• Experience working with international clients and/or distributed teams.
• English language proficiency at C1 level.
• Nice to have: Docker, Kubernetes or other containerization experience, and familiarity with RAG (Retrieval-Augmented Generation).